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Multiscale models of cell signaling.
Sameer S Bajikar1, Kevin A Janes
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
Annals of Biomedical Engineering
|April 6, 2012
Summary
Multiscale computational models are needed for cell signaling, bridging molecules, pathways, and networks. Integrating diverse models is key for understanding diseases like cancer.
Area of Science:
- Computational biology
- Systems biology
- Cellular signaling
Background:
- Computational models of signal transduction face challenges in resolving scales below the single-cell level.
- Existing models often struggle to bridge molecular, pathway, and network levels of cellular communication.
Purpose of the Study:
- To organize the challenges in multiscale modeling of cell signaling.
- To propose a framework for integrating different computational approaches across intracellular scales.
Main Methods:
- Categorization of challenges around three key interfaces: molecules to pathways, pathways to networks, and networks to outcomes.
- Analysis of computational approaches and data requirements for each interface.
Main Results:
- Each interface (molecules-pathways, pathways-networks, networks-outcomes) necessitates distinct computational methods and data.
- No single computational approach or dataset can effectively span all critical intracellular scales for cell signaling.
Conclusions:
- Realistic "whole-cell" models of cell signaling require the agglomeration of diverse model types across intracellular scales.
- Future multiscale models will be crucial for elucidating the effects of signaling mutations and population variants in diseases like cancer.
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